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445 lines
13 KiB
445 lines
13 KiB
#![allow(non_snake_case)]
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mod utils;
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use ndarray::arr3;
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use reCTBN::ctbn::*;
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use reCTBN::network::Network;
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use reCTBN::parameter_learning::*;
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use reCTBN::params;
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use reCTBN::tools::*;
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use utils::*;
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extern crate approx;
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use crate::approx::AbsDiffEq;
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fn learn_binary_cim<T: ParameterLearning>(pl: T) {
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let mut net = CtbnNetwork::new();
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let n1 = net
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.add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
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.unwrap();
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let n2 = net
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.add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
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.unwrap();
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net.add_edge(n1, n2);
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match &mut net.get_node_mut(n1) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]])));
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}
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}
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match &mut net.get_node_mut(n2) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-1.0, 1.0],
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[4.0, -4.0]
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],
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[
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[-6.0, 6.0],
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[2.0, -2.0]
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],
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]))
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);
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}
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}
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let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
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let p = match pl.fit(&net, &data, 1, None) {
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params::Params::DiscreteStatesContinousTime(p) => p,
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};
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assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
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assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
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&arr3(&[
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[
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[-1.0, 1.0],
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[4.0, -4.0]
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],
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[
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[-6.0, 6.0],
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[2.0, -2.0]
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],
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]),
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0.1
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));
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}
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#[test]
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fn learn_binary_cim_MLE() {
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let mle = MLE {};
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learn_binary_cim(mle);
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}
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#[test]
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fn learn_binary_cim_BA() {
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let ba = BayesianApproach { alpha: 1, tau: 1.0 };
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learn_binary_cim(ba);
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}
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fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
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let mut net = CtbnNetwork::new();
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let n1 = net
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.add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
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.unwrap();
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let n2 = net
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.add_node(generate_discrete_time_continous_node(String::from("n2"), 3))
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.unwrap();
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net.add_edge(n1, n2);
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match &mut net.get_node_mut(n1) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-3.0, 2.0, 1.0],
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[1.5, -2.0, 0.5],
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[0.4, 0.6, -1.0]
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],
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]))
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);
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}
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}
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match &mut net.get_node_mut(n2) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-1.0, 0.5, 0.5],
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[3.0, -4.0, 1.0],
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[0.9, 0.1, -1.0]
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],
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[
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[-6.0, 2.0, 4.0],
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[1.5, -2.0, 0.5],
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[3.0, 1.0, -4.0]
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],
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[
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[-1.0, 0.1, 0.9],
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[2.0, -2.5, 0.5],
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[0.9, 0.1, -1.0]
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],
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]))
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);
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}
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}
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let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
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let p = match pl.fit(&net, &data, 1, None) {
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params::Params::DiscreteStatesContinousTime(p) => p,
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};
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assert_eq!(p.get_cim().as_ref().unwrap().shape(), [3, 3, 3]);
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assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
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&arr3(&[
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[
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[-1.0, 0.5, 0.5],
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[3.0, -4.0, 1.0],
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[0.9, 0.1, -1.0]
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],
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[
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[-6.0, 2.0, 4.0],
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[1.5, -2.0, 0.5],
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[3.0, 1.0, -4.0]
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],
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[
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[-1.0, 0.1, 0.9],
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[2.0, -2.5, 0.5],
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[0.9, 0.1, -1.0]
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],
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]),
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0.1
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));
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}
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#[test]
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fn learn_ternary_cim_MLE() {
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let mle = MLE {};
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learn_ternary_cim(mle);
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}
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#[test]
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fn learn_ternary_cim_BA() {
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let ba = BayesianApproach { alpha: 1, tau: 1.0 };
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learn_ternary_cim(ba);
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}
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fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
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let mut net = CtbnNetwork::new();
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let n1 = net
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.add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
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.unwrap();
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let n2 = net
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.add_node(generate_discrete_time_continous_node(String::from("n2"), 3))
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.unwrap();
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net.add_edge(n1, n2);
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match &mut net.get_node_mut(n1) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-3.0, 2.0, 1.0],
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[1.5, -2.0, 0.5],
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[0.4, 0.6, -1.0]
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]
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]))
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);
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}
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}
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match &mut net.get_node_mut(n2) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-1.0, 0.5, 0.5],
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[3.0, -4.0, 1.0],
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[0.9, 0.1, -1.0]
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],
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[
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[-6.0, 2.0, 4.0],
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[1.5, -2.0, 0.5],
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[3.0, 1.0, -4.0]
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],
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[
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[-1.0, 0.1, 0.9],
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[2.0, -2.5, 0.5],
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[0.9, 0.1, -1.0]
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],
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]))
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);
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}
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}
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let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
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let p = match pl.fit(&net, &data, 0, None) {
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params::Params::DiscreteStatesContinousTime(p) => p,
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};
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assert_eq!(p.get_cim().as_ref().unwrap().shape(), [1, 3, 3]);
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assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
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&arr3(&[
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[
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[-3.0, 2.0, 1.0],
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[1.5, -2.0, 0.5],
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[0.4, 0.6, -1.0]
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],
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]),
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0.1
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));
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}
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#[test]
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fn learn_ternary_cim_no_parents_MLE() {
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let mle = MLE {};
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learn_ternary_cim_no_parents(mle);
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}
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#[test]
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fn learn_ternary_cim_no_parents_BA() {
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let ba = BayesianApproach { alpha: 1, tau: 1.0 };
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learn_ternary_cim_no_parents(ba);
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}
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fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
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let mut net = CtbnNetwork::new();
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let n1 = net
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.add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
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.unwrap();
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let n2 = net
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.add_node(generate_discrete_time_continous_node(String::from("n2"), 3))
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.unwrap();
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let n3 = net
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.add_node(generate_discrete_time_continous_node(String::from("n3"), 4))
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.unwrap();
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net.add_edge(n1, n2);
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net.add_edge(n1, n3);
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net.add_edge(n2, n3);
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match &mut net.get_node_mut(n1) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-3.0, 2.0, 1.0],
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[1.5, -2.0, 0.5],
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[0.4, 0.6, -1.0]
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],
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]))
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);
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}
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}
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match &mut net.get_node_mut(n2) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-1.0, 0.5, 0.5],
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[3.0, -4.0, 1.0],
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[0.9, 0.1, -1.0]
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],
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[
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[-6.0, 2.0, 4.0],
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[1.5, -2.0, 0.5],
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[3.0, 1.0, -4.0]
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],
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[
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[-1.0, 0.1, 0.9],
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[2.0, -2.5, 0.5],
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[0.9, 0.1, -1.0]
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],
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]))
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);
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}
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}
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match &mut net.get_node_mut(n3) {
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params::Params::DiscreteStatesContinousTime(param) => {
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assert_eq!(
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Ok(()),
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param.set_cim(arr3(&[
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[
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[-1.0, 0.5, 0.3, 0.2],
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[0.5, -4.0, 2.5, 1.0],
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[2.5, 0.5, -4.0, 1.0],
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[0.7, 0.2, 0.1, -1.0]
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],
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[
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[-6.0, 2.0, 3.0, 1.0],
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[1.5, -3.0, 0.5, 1.0],
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[2.0, 1.3, -5.0, 1.7],
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[2.5, 0.5, 1.0, -4.0]
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],
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[
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[-1.3, 0.3, 0.1, 0.9],
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[1.4, -4.0, 0.5, 2.1],
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[1.0, 1.5, -3.0, 0.5],
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[0.4, 0.3, 0.1, -0.8]
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],
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[
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[-2.0, 1.0, 0.7, 0.3],
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[1.3, -5.9, 2.7, 1.9],
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[2.0, 1.5, -4.0, 0.5],
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[0.2, 0.7, 0.1, -1.0]
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],
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[
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[-6.0, 1.0, 2.0, 3.0],
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[0.5, -3.0, 1.0, 1.5],
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[1.4, 2.1, -4.3, 0.8],
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[0.5, 1.0, 2.5, -4.0]
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],
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[
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[-1.3, 0.9, 0.3, 0.1],
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[0.1, -1.3, 0.2, 1.0],
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[0.5, 1.0, -3.0, 1.5],
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[0.1, 0.4, 0.3, -0.8]
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],
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[
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[-2.0, 1.0, 0.6, 0.4],
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[2.6, -7.1, 1.4, 3.1],
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[5.0, 1.0, -8.0, 2.0],
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[1.4, 0.4, 0.2, -2.0]
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],
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[
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[-3.0, 1.0, 1.5, 0.5],
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[3.0, -6.0, 1.0, 2.0],
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[0.3, 0.5, -1.9, 1.1],
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[5.0, 1.0, 2.0, -8.0]
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],
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[
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[-2.6, 0.6, 0.2, 1.8],
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[2.0, -6.0, 3.0, 1.0],
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[0.1, 0.5, -1.3, 0.7],
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[0.8, 0.6, 0.2, -1.6]
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],
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]))
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);
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}
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}
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let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259));
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let p = match pl.fit(&net, &data, 2, None) {
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params::Params::DiscreteStatesContinousTime(p) => p,
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};
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assert_eq!(p.get_cim().as_ref().unwrap().shape(), [9, 4, 4]);
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assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
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&arr3(&[
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[
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[-1.0, 0.5, 0.3, 0.2],
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[0.5, -4.0, 2.5, 1.0],
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[2.5, 0.5, -4.0, 1.0],
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[0.7, 0.2, 0.1, -1.0]
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],
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[
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[-6.0, 2.0, 3.0, 1.0],
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[1.5, -3.0, 0.5, 1.0],
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[2.0, 1.3, -5.0, 1.7],
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[2.5, 0.5, 1.0, -4.0]
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],
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[
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[-1.3, 0.3, 0.1, 0.9],
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[1.4, -4.0, 0.5, 2.1],
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[1.0, 1.5, -3.0, 0.5],
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[0.4, 0.3, 0.1, -0.8]
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],
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[
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[-2.0, 1.0, 0.7, 0.3],
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[1.3, -5.9, 2.7, 1.9],
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[2.0, 1.5, -4.0, 0.5],
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[0.2, 0.7, 0.1, -1.0]
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],
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[
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[-6.0, 1.0, 2.0, 3.0],
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[0.5, -3.0, 1.0, 1.5],
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[1.4, 2.1, -4.3, 0.8],
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[0.5, 1.0, 2.5, -4.0]
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],
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[
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[-1.3, 0.9, 0.3, 0.1],
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[0.1, -1.3, 0.2, 1.0],
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[0.5, 1.0, -3.0, 1.5],
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[0.1, 0.4, 0.3, -0.8]
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],
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[
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[-2.0, 1.0, 0.6, 0.4],
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[2.6, -7.1, 1.4, 3.1],
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[5.0, 1.0, -8.0, 2.0],
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[1.4, 0.4, 0.2, -2.0]
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],
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[
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[-3.0, 1.0, 1.5, 0.5],
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[3.0, -6.0, 1.0, 2.0],
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[0.3, 0.5, -1.9, 1.1],
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[5.0, 1.0, 2.0, -8.0]
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],
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[
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[-2.6, 0.6, 0.2, 1.8],
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[2.0, -6.0, 3.0, 1.0],
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[0.1, 0.5, -1.3, 0.7],
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[0.8, 0.6, 0.2, -1.6]
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],
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]),
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0.2
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));
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}
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#[test]
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fn learn_mixed_discrete_cim_MLE() {
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let mle = MLE {};
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learn_mixed_discrete_cim(mle);
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}
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#[test]
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fn learn_mixed_discrete_cim_BA() {
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let ba = BayesianApproach { alpha: 1, tau: 1.0 };
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learn_mixed_discrete_cim(ba);
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}
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